Relevant bibliographies by topics / Energy Management Strategy (EMS)

Academic literature on the topic 'Energy Management Strategy (EMS)'

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Published: 14 March 2023

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Journal articles on the topic "Energy Management Strategy (EMS)"

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Yao, Dongwei, Xinwei Lu, Xiangyun Chao, Yongguang Zhang, Junhao Shen, Fanlong Zeng, Ziyan Zhang, and Feng Wu. "Adaptive Equivalent Fuel Consumption Minimization Based Energy Management Strategy for Extended-Range Electric Vehicle." Sustainability 15, no. 5 (March 4, 2023): 4607. http://dx.doi.org/10.3390/su15054607.

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Unlike battery electric vehicles, extended-range electric vehicles have one more energy source, so a reasonable energy management strategy (EMS) is crucial to the fuel economy of the vehicles. In this paper, an adaptive equivalent fuel consumption minimization strategy (A-ECMS)-based energy management strategy is proposed for the extended-range electric vehicle. The equivalent fuel consumption minimization strategy (ECMS), which utilizes Pontryagin’s minimum principle (PMP), is introduced to design the EMS. Compared with other ECMS strategies, an adaptive equivalent factor algorithm, based on state of charge (SOC) feedback and a proportional–integral (PI) controller is designed to update the equivalent factor under different working conditions. Additionally, a start–stop penalty is added to the objective function to take the dynamic start–stop process of the range extender into account. As a result, under the WLTC driving cycle, the proposed strategy can achieve 6.78 L/100 km comprehensive fuel consumption, saving 6.2% and 3.4% fuel consumption compared with the conventional rule-based thermostat strategy and the power following strategy. Moreover, the proposed EMS achieves the lowest ampere-hour flux among the three EMSs, indicating its ability to improve battery life.
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Chen, Xu, Guangdi Hu, Feng Guo, Mengqi Ye, and Jingyuan Huang. "Switched Energy Management Strategy for Fuel Cell Hybrid Vehicle Based on Switch Network." Energies 13, no. 1 (January 3, 2020): 247. http://dx.doi.org/10.3390/en13010247.

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Environmentally friendly and pollution-free fuel cell/lithium battery hybrid vehicles have received the attention of the community in recent years. It is imperative for fuel cell/lithium battery hybrid vehicles to use the energy management strategy (EMS) to distribute the output power of each power source to improve fuel economy and system life. In practical application, inconsistency of battery pack will lead to security hazard and capacity degradation. However, few EMS take the inconsistency of battery pack into account. Also, the current battery equalization strategy rarely discusses how to perform the equilibrium process while meeting the power demand of vehicle. To solve these issues, a novel equalization energy management strategy (EEMS) based on the switch network is proposed at first. Then, a switched energy management strategy (SEMS) that switches between the EEMS and the equivalent consumption minimization strategy (ECMS) is proposed and implemented in the fuel cell/lithium battery hybrid system to validate its effectiveness. The results show that the proposed SEMS can ameliorate the inconsistency of series lithium battery pack while meeting the power demand of vehicle’s normal operation. It can improve the safety and durability of the system and reduce the equalization time. Besides, it has good expansibility and no energy waste.
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Michel, Pierre, Alain Charlet, Guillaume Colin, Yann Chamaillard, Cédric Nouillant, and Gérard Bloch. "3WCC Temperature Integration in a Gasoline-HEV Optimal Energy Management Strategy." Advances in Mechanical Engineering 6 (January 1, 2014): 802597. http://dx.doi.org/10.1155/2014/802597.

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For a gasoline-hybrid electric vehicle (HEV), the energy management strategy (EMS) is the computation of the distribution between electric and gasoline propulsion. Until recently, the EMS objective was to minimize fuel consumption. However, decreasing fuel consumption does not directly minimize the pollutant emissions, and the 3-way catalytic converter (3WCC) must be taken into account. This paper proposes to consider the pollutant emissions in the EMS, by minimizing, with the Pontryagin minimum principle, a tradeoff between pollution and fuel consumption. The integration of the 3WCC temperature in the EMS is discussed and finally a simplification is proposed.
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Bakht, Muhammad Paend, Zainal Salam, Abdul Rauf Bhatti, Waqas Anjum, Saifulnizam A. Khalid, and Nuzhat Khan. "Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding." Applied Sciences 11, no. 10 (May 18, 2021): 4601. http://dx.doi.org/10.3390/app11104601.

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This study investigates the potential application of Stateflow (SF) to design an energy management strategy (EMS) for a renewable-based hybrid energy system (HES). The SF is an extended finite state machine; it provides a platform to design, model, and execute complex event-driven systems using an interactive graphical environment. The HES comprises photovoltaics (PV), energy storage units (ESU) and a diesel generator (Gen), integrated with the power grid that experiences a regular load shedding condition (scheduled power outages). The EMS optimizes the energy production and utilization during both modes of HES operation, i.e., grid-connected mode and the islanded mode. For islanded operation mode, a resilient power delivery is ensured when the system is subjected to intermittent renewable supply and grid vulnerability. The contributions of this paper are twofold: first is to propose an integrated framework of HES to address the problem of load shedding, and second is to design and implement a resilient EMS in the SF environment. The validation of the proposed EMS demonstrates its feasibility to serve the load for various operating scenarios. The latter include operations under seasonal variation, abnormal weather conditions, and different load shedding patterns. The simulation results reveal that the proposed EMS not only ensures uninterrupted power supply during load shedding but also reduces grid burden by maximizing the use of PV energy. In addition, the SF-based adopted methodology is envisaged to be a useful alternative to the popular design method using the conventional software tools, particularly for event-driven systems.
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Ferahtia, Seydali, Hegazy Rezk, Rania M. Ghoniem, Ahmed Fathy, Reem Alkanhel, and Mohamed M. Ghonem. "Optimal Energy Management for Hydrogen Economy in a Hybrid Electric Vehicle." Sustainability 15, no. 4 (February 10, 2023): 3267. http://dx.doi.org/10.3390/su15043267.

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Fuel cell hybrid electric vehicles (FCEVs) are mainly electrified by the fuel cell (FC) system. As a supplementary power source, a battery or supercapacitor (SC) is employed (besides the FC) to enhance the power response due to the slow dynamics of the FC. Indeed, the performance of the hybrid power system mainly depends on the required power distribution manner among the sources, which is managed by the energy management strategy (EMS). This paper considers an FCEV based on the proton exchange membrane FC (PEMFC)/battery/SC. The energy management strategy is designed to ensure optimum power distribution between the sources considering hydrogen consumption. Its main objective is to meet the electric motor’s required power with economic hydrogen consumption and better electrical efficiency. The proposed EMS combines the external energy maximization strategy (EEMS) and the bald eagle search algorithm (BES). Simulation tests for the Extra-Urban Driving Cycle (EUDC) and New European Driving Cycle (NEDC) profiles were performed. The test is supposed to be performed in typical conditions t = 25 °C on a flat road without no wind effect. In addition, this strategy was compared with the state machine control strategy, classic PI, and equivalent consumption minimization strategy. In terms of optimization, the proposed approach was compared with the original EEMS, particle swarm optimization (PSO)-based EEMS, and equilibrium optimizer (EO)-based EEMS. The results confirm the ability of the proposed strategy to reduce fuel consumption and enhance system efficiency. This strategy provides 26.36% for NEDC and 11.35% for EUDC fuel-saving and efficiency enhancement by 6.74% for NEDC and 36.19% for EUDC.
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Li, Pei, Jun Yan, Qunzhang Tu, Ming Pan, and Jinhong Xue. "A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle." Mathematical Problems in Engineering 2018 (October 29, 2018): 1–15. http://dx.doi.org/10.1155/2018/8450213.

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The performance and fuel consumption of hybrid electric vehicle heavily depend on the EMS (energy management strategy). This paper presents a novel EMS for a series hybrid electric rescue vehicle. Firstly, considering the working characteristics of engine and battery, the EMS combining logic threshold and fuzzy control is proposed. Secondly, a fuzzy control optimization method based on IQGA (improved quantum genetic algorithm) is designed to achieve better fuel efficiency. Then, the modeling and simulation are completed by using MATLAB/Simulink; the results demonstrate that the fuel consumption can be decreased by 5.17% after IQGA optimization and that the optimization effect of IQGA is better than that of GA (genetic algorithm) and QGA (quantum genetic algorithm). Finally, the HILS (hardware in loop simulation) platform is constructed with dSPACE; the HILS experiment shows that the proposed EMS can effectively improve the vehicle working efficiency, which can be applied to practical application.
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Hu, Tengda, Yunwu Li, Zhi Zhang, Ying Zhao, and Dexiong Liu. "Energy Management Strategy of Hybrid Energy Storage System Based on Road Slope Information." Energies 14, no. 9 (April 21, 2021): 2358. http://dx.doi.org/10.3390/en14092358.

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To maximize the performance of power batteries and supercapacitors in a hybrid energy storage system (HESS) and to resolve the conflict between the high power demands of electric vehicles and the limitations of high-current charging and discharging of the power battery, a vehicle power demand model incorporating road slope information has been constructed. This paper takes a HESS composed of power battery and supercapacitor as the object, and a rule-based energy management strategy (EMS) based on road slope information is proposed to realize the reasonable distribution and management of energy under the slope condition. According to the slope information of the road ahead, the energy consumption in the next period was predicted, and the supercapacitor is charged and discharged in advance to meet the energy demand of uphill and the energy recovery capacity of downhill to avoid the high current charge and discharge of the battery. Subsequently, the improved EMS performance was simulated under the New York City Cycle (NYCC) driving conditions with additional slope driving conditions. The simulated results indicate that compared to the existing EMS, the proposed EMS based on slope information can effectively distribute the power demand between the power battery and the supercapacitor, can reduce the discharge current and the duration of high-power discharge, and has a 20.4% higher energy recovery efficiency, effectively increasing the cruising range.
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Wang, Yaqian, and Xiaohong Jiao. "Dual Heuristic Dynamic Programming Based Energy Management Control for Hybrid Electric Vehicles." Energies 15, no. 9 (April 28, 2022): 3235. http://dx.doi.org/10.3390/en15093235.

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This paper investigates an adaptive dynamic programming (ADP)-based energy management control strategy for a series-parallel hybrid electric vehicle (HEV). This strategy can further minimize the equivalent fuel consumption while satisfying the battery level constraints and vehicle power demand. Dual heuristic dynamic programming (DHP) is one of the basic structures of ADP, combining reinforcement learning, dynamic programming (DP) optimization principle, and neural network approximation function, which has higher accuracy with a slightly more complex structure. In this regard, the DHP energy management strategy (EMS) is designed by the backpropagation neural network (BPNN) as an Action network and two Critic networks approximating the control policy and the gradient of value function concerning the state variable. By comparing with the existing results such as HDP-based and rule-based control strategies, the equivalent consumption minimum strategy (ECMS), and reinforcement learning (RL)-based strategy, simulation results verify the robustness of fuel economy and the adaptability of the power-split optimization of the proposed EMS to different driving conditions.
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Abdulhadi Abdulsalam Abulifa, Azura Che Soh, Mohd Khair Hassan, Raja Mohd Kamil Raja Ahmad, and Mohd Amran Mohd Radzi. "Control strategies for energy management system in electric vehicle using high-level supervisory control." International Journal of Science and Technology Research Archive 3, no. 2 (October 30, 2022): 037–44. http://dx.doi.org/10.53771/ijstra.2022.3.2.0116.

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Energy Management System (EMS) is a computer-supported device utilized by drivers of electrical frameworks to maintain management and to optimize the efficiency of transmission systems. In this paper, a control strategy for EMS using on the High-level Supervisory Control (HLSC) has been reviewed. This HLSC strategy with an intelligent management algorithm technique has been evolving rapidly particularly in EMS for Electrical Vehicles (EVs). Their revolutionary applications provide efficient control strategies for EMS that increase capabilities, efficiency and accuracy, as well as reducing energy consumption in EVs. Applying EMS with HLSC control strategy with an intelligent management algorithm that is able reallocate the electrical power flow inside the EVs system to boost power efficiency and obtain optimum effectiveness. Such innovative solutions can enhance the efficiency of smart EMS in EVs as the future sustainable transportation.
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Wang, Hao, Hongwen He, Jianwei Li, Yunfei Bai, Yuhua Chang, and Beizhan Yan. "Adaptive MPC Based Real-Time Energy Management Strategy of the Electric Sanitation Vehicles." Applied Sciences 11, no. 2 (January 6, 2021): 498. http://dx.doi.org/10.3390/app11020498.

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Electric sanitation vehicles have increasingly been applied to cleaning work due to the requirement of air pollution control. The power distribution and energy management strategy (EMS) influence the vehicle’s performance a lot both in the aspects of cleaning effect and electricity consumption. Aiming to improve energy economy and ensure clean tasks, first, the electricity consumption percentages of the vehicle onboard devices are analyzed and the main contributors are clarified, and the power requirement model of the working motor is built based on experimental data. Second, a universal modeling method of garbage distribution on the road surface is proposed, which implements a nonlinear autoregressive neural network as the predictor. Third, an adaptive model predictive control (AMPC)-based EMS is proposed and verified. The results show the AMPC method can accurately predict the garbage density and the proposed EMS can approximate the energy consumption of the DP-based EMS with little deviation. Compared to conventional rule-based EMS, the AMPC-based EMS achieved a 15.5% decrease in energy consumption as well as a 14.6% decrease in working time.
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Dissertations / Theses on the topic "Energy Management Strategy (EMS)"

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Fletcher, Thomas P. "Optimal energy management strategy for a fuel cell hybrid electric vehicle." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/25567.

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The Energy Management Strategy (EMS) has a huge effect on the performance of any hybrid vehicle because it determines the operating point of almost every component associated with the powertrain. This means that its optimisation is an incredibly complex task which must consider a number of objectives including the fuel consumption, drive-ability, component degradation and straight-line performance. The EMS is of particular importance for Fuel Cell Hybrid Electric Vehicles (FCHEVs), not only to minimise the fuel consumption, but also to reduce the electrical stress on the fuel cell and maximise its useful lifetime. This is because the durability and cost of the fuel cell stack is one of the major obstacles preventing FCHEVs from being competitive with conventional vehicles. In this work, a novel EMS is developed, specifcally for Fuel Cell Hybrid Electric Vehicles (FCHEVs), which considers not only the fuel consumption, but also the degradation of the fuel cell in order to optimise the overall running cost of the vehicle. This work is believed to be the first of its kind to quantify effect of decisions made by the EMS on the fuel cell degradation, inclusive of multiple causes of voltage degradation. The performance of this new strategy is compared in simulation to a recent strategy from the literature designed solely to optimise the fuel consumption. It is found that the inclusion of the degradation metrics results in a 20% increase in fuel cell lifetime for only a 3.7% increase in the fuel consumption, meaning that the overall running cost is reduced by 9%. In addition to direct implementation on board a vehicle, this technique for optimising the degradation alongside the fuel consumption also allows alternative vehicle designs to be compared in an unbiased way. In order to demonstrate this, the novel optimisation technique is subsequently used to compare alternative system designs in order to identify the optimal economic sizing of the fuel cell and battery pack. It is found that the overall running cost can be minimised by using the smallest possible fuel cell stack that will satisfy the average power requirement of the duty cycle, and by using an oversized battery pack to maximise the fuel cell effciency and minimise the transient loading on the stack. This research was undertaken at Loughborough University as part of the Doctoral Training Centre (DTC) in Hydrogen, Fuel Cells and Their Applications in collaboration with the University of Birmingham and Nottingham University and with sponsorship from HORIBA-MIRA (Nuneaton, UK). A Microcab H4 test vehicle has been made available for use in testing for this research which was previously used for approximately 2 years at the University of Birmingham. The Microcab H4 is a small campus based vehicle designed for passenger transport and mail delivery at low speeds as seen on a university campus. It has a top speed of approximately 30mph, and is fitted with a 1.2kW fuel cell and a 2kWh battery pack.
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Caramia, Gabriele. "Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11755/.

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Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.
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Hägglund, Andreas, and Moa Källgren. "Impact of Engine Dynamics on Optimal Energy Management Strategies for Hybrid Electric Vehicles." Thesis, Linköpings universitet, Fordonssystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148890.

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In recent years, rules and regulations regarding fuel consumption of vehicles and the amount of emissions produced by them are becoming stricter. This has led the automotive industry to develop more advanced solutions to propel vehicles to meet the legal requirements. The Hybrid Electric Vehicle is one of the solutions that is becoming more popular in the automotive industry. It consists of an electrical driveline combined with a conventional powertrain, propelled by either a diesel or petrol engine. Two power sources create the possibility to choose when and how to use the power sources to propel the vehicle. The strategy that decides how this is done is referred to as an energy management strategy. Today most energy management strategies only try to reduce fuel consumption using models that describe the steady state behaviour of the engine. In other words, no reduction of emissions is achieved and all transient behaviour is considered negligible.  In this thesis, an energy management strategy incorporating engine dynamics to reduce fuel consumption and nitrogen oxide emissions have been designed. First, the models that describe how fuel consumption and nitrogen oxide emissions behave during transient engine operation are developed. Then, an energy management strategy is developed consisting of a model predictive controller that combines the equivalent consumption minimization strategy and convex optimization. Results indicate that by considering engine dynamics in the energy management strategy, both fuel consumption and nitrogen oxide emissions can be reduced. Furthermore, it is also shown that the major reduction in fuel consumption and nitrogen oxide emissions is achieved for short prediction horizons.
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Almgren, Johan, and Gustav Elingsbo. "Route Based Optimal Control Strategy for Plug-In Hybrid Electric Vehicles." Thesis, Linköpings universitet, Fordonssystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138713.

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More restrictive emission legislations, rising fuel prices and the realisation that oil is a limited resource have lead to the emergence of the hybrid electric vehicles.To fully utilise the potential of the hybrid electric vehicles, energy management strategies are needed. The main objective of the strategy is to ensure that the limited electric energy is utilised in an efficient manner.This thesis develops and evaluates an optimisation based energy management strategy for plug-in hybrid electric vehicles. The optimisation methods used are based on a dynamic programming and ECMS approach. The strategy is validated against Vsim, Volvo Cars' performance and fuel consumption analysis tool as well as against strategies where parts of the optimisation is replaced by logic. The results show that the developed strategy consumes less fuel both compared to the corresponding Vsim strategy and the logic strategies.
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Wollaeger, James P. "ITS in Energy Management Systems of PHEV's." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1330704818.

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Pahkasalo, Carolina, and André Sollander. "Adaptive Energy Management Strategies for Series Hybrid Electric Wheel Loaders." Thesis, Linköpings universitet, Fordonssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166284.

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An emerging technology is the hybridization of wheel loaders. Since wheel loaders commonly operate in repetitive cycles it should be possible to use this information to develop an efficient energy management strategy that decreases fuel consumption. The purpose of this thesis is to evaluate if and how this can be done in a real-time online application. The strategy that is developed is based on pattern recognition and Equivalent Consumption Minimization Strategy (ECMS), which together is called Adaptive ECMS (A-ECMS). Pattern recognition uses information about the repetitive cycles and predicts the operating cycle, which can be done with Neural Network or Rule-Based methods. The prediction is then used in ECMS to compute the optimal power distribution of fuel and battery power. For a robust system it is important with stability implementations in ECMS to protect the machine, which can be done by adjusting the cost function that is minimized. The result from these implementations in a quasistatic simulation environment is an improvement in fuel consumption by 7.59 % compared to not utilizing the battery at all.
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Reichenwallner, Christopher, and Daniel Wasborg. "Control of a Hydraulic Hybrid System for Wheel Loaders." Thesis, Linköpings universitet, Fluida och mekatroniska system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158902.

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In recent years many companies have investigated the use of hybrid technology due to the potential of increasing the driveline’s efficiency and thus reducing fuel consumption. Previous studies show that hydraulic hybrid technology can be favourable to use in construction machinery such as wheel loaders, which often operate in repetitive drive cycles and have high transient power demands. Parallel as well as Series hybrid configurations are both found suitable for wheel loader applications as the hybrid configurations can decrease the dependency on the torque converter. This project has investigated a novel hydraulic hybrid concept which utilizes the wheel loaders auxiliary pump as a supplement to enable both Series and Parallel hybrid operation. Impact of accumulator sizes has also been investigated, for which smaller accumulator sizes resembles a hydrostatic transmission. The hybrid concept has been evaluated by developing a wheel loader simulation model and a control system based on a rule-based energy management strategy. Simulation results indicate improved energy efficiency of up to 18.80 % for the Combined hybrid. Moreover, the accumulator sizes prove to have less impact on the energy efficiency. A hybrid system with decreased accumulator sizes shows improved energy efficiency of up to 16.40 %.
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Bragagni, Cristiano. "Progettazione di uno strumento di analisi dati per sistemi SCADA-ENERGY Management System." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7927/.

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L’obiettivo di questa tesi è approfondire le competenze sulle funzionalità sviluppate nei sistemi SCADA/EMS presenti sul mercato, così da conoscerne le potenzialità offerte: tutte le conoscenze acquisite servono a progettare uno strumento di analisi dati flessibile e interattivo, con il quale è possibile svolgere analisi non proponibili con le altre soluzioni analizzate. La progettazione dello strumento di analisi dei dati è orientata a definire un modello multidimensionale per la rappresentazione delle informazioni: il percorso di progettazione richiede di individuare le informazioni d’interesse per l’utente, così da poterle reintrodurre in fase di progettazione della nuova base dati. L’infrastruttura finale di questa nuova funzionalità si concretizza in un data warehouse: tutte le informazioni di analisi sono memorizzare su una base dati diversa da quella di On.Energy, evitando di correlare le prestazione dei due diversi sottosistemi. L’utilizzo di un data warehouse pone le basi per realizzare analisi su lunghi periodi temporali: tutte le tipologie di interrogazione dati comprendono un enorme quantità d’informazioni, esattamente in linea con le caratteristiche delle interrogazioni OLAP
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Shafique, Hamza. "On Development and Optimization of Energy Management System (EMS) for Battery Energy Storage System (BESS) : Providing Ancillary Services." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289630.

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The battery energy storage systems (BESS) installed standalone and with solar photovoltaic installations can be used beyond just storing excess generated electricity from the solar panels. The BESS can be intelligently managed by an Energy Management System (EMS) that uses the BESS resource for multiple ancillary services. The hypothesis in this study is that by optimizing the distribution of BESS resource between peak shaving of local load and providing frequency regulation service through the reserve market additional value can be generated from the already present resource. The EMS designed during the course of this thesis consists of two main parts, first the Prognosis Module that forecasts and makes recommendation for the delivery of hourly service from the BESS with quantified uncertainty and, second the Realtime Operation Module that takes the recommendations from the Prognosis Module and dispatches the necessary service meanwhile correcting for the uncertainty from the Prognosis Module. The Prognosis Module of the EMS is tested through the Öckero Ice Rink case study. In the case study local peak shaving saves 9.5% of the monthly power tariff by reducing its demand component through shaving the peak power of the test day by 21%. The EMS also allows for profit generation by frequency regulation through reserving capacity for three hourly slots within the test day on the reserve market.
Ett batterilager installerat separat eller tillsammans med en solelanläggning kan användas för mer än att öka egenanvändning av solel. Smart styrning med ett Energy Management System (EMS) möjliggör leverans av systemtjänster från batterilagret till elnätet. Hypotesen i denna studie innefattar att optimering av distributionen av ett energilagers kapacitet mellan kapning av effekttoppar och leverans av systemtjänsten frekvensreglering innebär en ökning av resursens värde. EMS som designats under detta projekt består av två delar; dels en prognosmodul som prognostiserar energianvändning för att ge rekommendationer för distribuering av kapacitet, dels en modul som i realtid styr batteriet baserat på prognosmodulens rekommendationer och uppmätt data. Prognosmodulen har testats i en fallstudie av Öckerö Ishall. Fallstudiens resultat visar att EMS som konstruerats reducerar nätavgiften med 9,5% genom att minska dagens högsta effekttopp med 21%. Resultatet visar även att frekvensreglering kunde levereras under tre timmar samma dag, vilket skulle generera ytterligare intäkter.
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Marstorp, Jonathan, and Sten Trolle. "Optimering av solcellssystem och implementering av ett Energy Management System : Möjligheter för ett framtida bostadsområde." Thesis, Uppsala universitet, Fasta tillståndets fysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-258130.

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Riksbyggen, one of Sweden’s largest real estate companies, are planning to build a new residential area outside Uppsala, Sweden. Adjacent to the houses in the area, Riksbyggen are considering installing a 137 kWp photovoltaic (PV) system to supply the residents with renewable energy. In the first part of this study, the proposed PV system is analyzed based on benefits and profitability for the residents, using the software HOMER. Possibilities for including 3 kWp roof-mounted PV systems with varying azimuth as a complement to the larger system are evaluated. In the second part, options for implementing an Energy Management System (EMS) with battery storage or load shifting in the distribution grid using MATLAB. The system uses forecasting of PV generation, electricity prices and electricity demand to optimize the system control strategies. The results from the study indicate that installation of the 137 kWp PV system could lead to reduced average electricity costs of 21,1 % for the residents. If the system is completed with roof-mounted PV modules of varying azimuth for 12 % of the households in the area, the average electricity cost is reduced by 29,4 %in total. Implementing an EMS with energy storage in the distribution grid was not found to be an economically viable option, mainly due to high energy storage costs. The system control strategies can be improved by using forecasting. The economic benefits of load shifting for a single household were found to be too low to give incentive for system investment.
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Books on the topic "Energy Management Strategy (EMS)"

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Martin, Spence, and Thompson Roy, eds. The energy fix: Towards a socialist energy strategy. London: Pluto, 1986.

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Porter, Andy. The energy fix: Towards a socialist energy strategy. London: Pluto Press, 1986.

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1966-, Parello John, ed. IP-enabled energy management: A proven strategy for administering energy as a service. Hoboken, N.J: Wiley Pub., 2010.

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A Workforce strategy for Alberta's energy sector. Edmonton: Alberta Employment, Immigration and Industry, 2007.

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The strategy quest: Releasing the energy of manufacturing within a market driven strategy : 'a dynamic business story'. London: Pitman, 1994.

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The strategy quest: Releasing the energy of manufacturing within a market driven strategy : "a dynamic business story". Coventry: AMD Pub., 1998.

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Wenig, Michael M. Developing a "comprehensive energy strategy" with a capital "C". Calgary, Alberta, Canada: Canadian Institute of Resources Law, 2008.

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Investing in energy: Creating a new investment strategy to maximize your portfolio's return. New York: Palgrave Macmillan, 2014.

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H, Moffett Michael, ed. The global oil & gas industry: Management, strategy, and finance. Tulsa, Okla: PennWell, 2011.

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United States. Dept. of Energy., ed. Environmental restoration and waste management: Roadmaps, how the U.S. Department of Energy develops a cleanup strategy. [Washington, D.C.?]: The Dept., 1992.

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Book chapters on the topic "Energy Management Strategy (EMS)"

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Ghazali, Muhammad Syahmi, and Muhammad Ikram Mohd Rashid. "Random Search in Energy Management Strategy (EMS) for Hybrid Electric Vehicles." In Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018, 219–27. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3708-6_19.

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bin Mohd Rashid, Muhammad Ikram, Hamdan Daniyal, and Mohd Ashraf Ahmad. "Energy Management Strategy (EMS) for Hybrid Electric Vehicles Based on Safe Experimentation Dynamics (SED)." In Lecture Notes in Computer Science, 370–77. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26354-6_37.

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Mohd Rashid, Muhammad Ikram, Ahmad Amir Solihin Mohd Apandi, Hamdan Daniyal, and Mohd Ashraf Ahmad. "Hyperheuristics Trajectory Based Optimization for Energy Management Strategy (EMS) of Split Plug-In Hybrid Electric Vehicle." In Lecture Notes in Electrical Engineering, 837–48. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2317-5_69.

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Jin, Shunfu, and Wuyi Yue. "Energy-Efficient Task Scheduling Strategy." In Resource Management and Performance Analysis of Wireless Communication Networks, 405–22. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7756-7_20.

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Sun, Qiuye. "Coordinated Power Management Control Strategy for Interconnected AC and DC Microgrids." In Energy Internet and We-Energy, 93–127. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0523-8_4.

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Jin, Shunfu, and Wuyi Yue. "Energy-Efficient Virtual Machine Allocation Strategy." In Resource Management and Performance Analysis of Wireless Communication Networks, 423–43. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7756-7_21.

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Yuan, Yue, Rongjia He, Haibo Zhao, Enlong Li, Xuekun Li, and Chunmei Xu. "Rule-Based Energy Management Strategy for Multi-energy Drive System." In Lecture Notes in Electrical Engineering, 499–505. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2862-0_48.

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Zahoor, Saman, Nadeem Javaid, Anila Yasmeen, Isra Shafi, Asif Khan, and Zahoor Ali Khan. "Optimized Energy Management Strategy for Home and Office." In Advances in Internet, Data & Web Technologies, 237–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75928-9_21.

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Luo, Pan, Muyi Lin, Yong Chen, Li Zhao, and Bin Ma. "Energy Management Strategy for Hybrid Engineering Vehicles with Composite Energy Storage." In Green Intelligent Transportation Systems, 147–56. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0302-9_15.

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Zheng, Xuemei, Zhongshuai Zhang, Haoyu Li, and Yong Feng. "Based on Energy Router Energy Management Control Strategy in Micro-grid." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 329–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93479-8_24.

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Conference papers on the topic "Energy Management Strategy (EMS)"

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Pham, T. H., P. P. J. van den Bosch, J. T. B. A. Kessels, and R. G. M. Huisman. "Cost-Effective Energy Management for Hybrid Electric Heavy-Duty Truck Including Battery Aging." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3729.

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Battery temperature has large impact on battery power capability and battery life time. In Hybrid Electric Heavy-duty trucks (HEVs), the high-voltage battery is normally equipped with an active Battery Thermal Management System (BTMS) guaranteeing a desired battery life time. Since the BTMS can consume a substantial amount of energy, this paper aims at integrating the Energy Management Strategy (EMS) and BTMS to minimize the overall operational cost of the truck (considering diesel fuel cost and battery life time cost). The proposed on-line strategy makes use of the Equivalent Consumption Minimization Strategy (ECMS) along with a physics-based approach to optimize both the power split (between the Internal Combustion Engine (ICE) and the Motor Generator (MG)) and the BTMS’s operation. The strategy also utilizes a quasi-static battery cycle-life model taking into account the effects of battery power and battery temperature on the battery capacity loss. Simulation results present an appropriate strategy for EMS and BTMS integration, and demonstrate the trade-off between the total vehicle fuel consumption and the battery life time.
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Millo, Federico, Luciano Rolando, and Luca Pulvirenti. "Energy Management System Optimization Based on V2X Connectivity." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2020-adm-087.

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Fuel economy of Hybrid Electric Vehicles (HEVs) may be further improved by exploiting the increased connectivity level of next -generation vehicles. Minimization of HEVs fuel consumption is a global problem and its optimal solution inevitably entails the complete knowledge of the driving conditions. Hence, optimality can only be reached on a limited number of a priori known mission profiles, and never on real driving test cases . Thus, the capabilities of conventional Energy Management Systems (EMS) can be strongly enhanced by integrating the prediction of future vehicle speed into the powertrain control strategy. Vehicle-to-Everything (V2X) technology adoption paves the way for reliable future driving conditions forecasting. As a result, in this paper information derived from V2X connectivity was used to develop an innovative adaptation algorithm for an Equivalent Consumption Minimization Strategy (ECMS). Traffic information and driving style identification were employed to predict future driving conditions and, in turn, to adapt the equivalence factor. Hence, some innovative correction parameters were introduced in the equivalence factor formulation, in order to periodically adapt it according to the predicted vehicle speed. The continuous equivalence factor optimization was aimed at ensuring enhanced fuel economy and at guaranteeing charge sustainability. The potential of this innovative Adaptive ECMS (A-ECMS) was assessed on a P2 architecture test case by means of numerical simulation. The reliability of the simulation platform had been preliminarily validated by comparing simulation results with experimental data. The experimental measurements were obtained by testing a Mercedes-Benz E 300 de on real-world driving scenarios. The simulation results proved that the proposed approach is able to significantly improve the strategy adaptability and its fuel economy potential if compared with the conventional EMS taken as reference. Fuel consumption reductions up to 10 % were demonstrated, depending on the vehicle mission profile. Finally, a sensitivity analysis was performed in order to assess how different prediction horizons affect the adaptive algorithm.
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Wang, Feng, Mohd Azrin Mohd Zulkefli, Zongxuan Sun, and Kim A. Stelson. "Investigation on the Energy Management Strategy for Hydraulic Hybrid Wheel Loaders." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3949.

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Energy management strategies for a hydraulic hybrid wheel loader are studied in this paper. The architecture of the hydraulic hybrid wheel loader is first presented and the differences of the powertrain and the energy management system between on-road vehicles and wheel loaders are identified. Unlike the on-road vehicles where the engine only powers the drivetrain, the engine in a wheel loader powers both the drivetrain and the working hydraulic system. In a non-hybrid wheel loader, the two sub-systems interfere with each other since they share the same engine shaft. By using a power split drivetrain, it not only allows for optimal engine operation and regenerative braking, but also eliminates interferences between driving and working functions, which improve the productivity, fuel efficiency and operability of the wheel loader. An energy management strategy (EMS) based on dynamic programming (DP) is designed to optimize the operation of both the power split drivetrain and the working hydraulic system. A short loading cycle is selected as the duty cycle. The EMS based on DP is compared with a rule-based strategy through simulation.
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Maroto, Pedro Manuel, Daniela De Lima, Danilo José Mendes, Marco Mammetti, Peter H. Bauer, and Joan Calres Bruno. "Development of Hybrid real-time capable Vehicle Simulation Platform for Energy Management Strategy Development and Virtual Validation." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2021-adm-143.

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Model-based calibration enables a shift in development tasks from the real world to the virtual world, allowing for increased system robustness while reducing development costs and time. These benefits are especially pronounced in the case of complex powertrain systems such as Hybrid Electric Vehicles (HEV). In (P)-HEV, Energy Management Strategy (EMS) constitutes the core for fuel consumption and emissions reduction of hybrid electric vehicles. This paper presents an assessment of the feasibility of carrying out automated model calibration within a virtual powertrain test environment for EMS validation. It highlights the accuracy of the engine simulation model under steady-state and transient operating conditions. It also demonstrates the integration of the model with data based pollutant emission models and the after-treatment system. The Internal Combustion Engine (ICE) is modeled by means of the so called Mean Value Engine Model approach (MVEM) with limited amount of input signals. Electrical components are modeled following a classical map-based approach. The novelty of this methodology resides in the pollutant emissions models. Engine out emissions (CO2, CO, HC and NO) are modeled through convolutional neural networks (CNN) giving extremely accurate results, both in instantaneous and cumulative prediction. This allows to calibrate a Three-Way Catalyst model (TWC) enabling the characterization of a complete powertrain system model with fuel consumption, emissions (CO2, CO, HC and NOx) and the electrification part. In this framework EMS can be developed for minimizing not only fuel consumption but also pollutant emissions. As final step, advanced EMS based on reinforcement learning is proposed. The methodology is developed in a co-simulation framework between MATLAB-Simulink and AMESIM. The resulting model runs between 2-3 times faster than real time in an off-the-shelf laptop. This enables the methodology for developing models suitable for HIL (hardware-in-the-loop) and SIL (software-in-the-loop) applications. The final error in predicted pollutant emissions in the studied cycles remains below 2.5% in the case of CO2 emissions, 3.5% in the case of NOx emissions, and below 8.5% when speaking about CO and HC emissions.
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Liu, Yongfei, Longlong Zhu, Fazhan Tao, and Zhumu Fu. "Energy Management Strategy of FCHEV Based on ECMS Method." In ICNCC 2019: 2019 The 8th International Conference on Networks, Communication and Computing. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3375998.3376019.

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Maharaj, Priya S., Shyam Dyal, and Kelvin Ramnath. "Development and Implementation of an Environmental Management System for an Oil Company in South Trinidad, in Accordance With the ISO 14001 Standard." In ASME 2001 Engineering Technology Conference on Energy. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/etce2001-17081.

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Abstract Trinidad and Tobago has the oldest petroleum industry in the world. The activities of this industry have historically impacted the environment and newly drafted environmental rules and regulations of Trinidad and Tobago will require sound environmental management systems by all operating companies in the industry. In order to meet the challenges of these new regulations, the Petroleum Company of Trinidad and Tobago Limited (Petrotrin) is currently pursuing the development and implementation of an Environmental Management System within its Exploration and Production operations in accordance with the ISO 14001 Standard. This project was initiated in October 1999, and is expected to terminate in December 2001 with international certification. Petrotrin, realizing the competitive advantages that can result from the integration of an Environmental Management System into its operating systems, has placed the implementation of the EMS as a company strategic goal. The project was categorized into three main stages: Development, Implementation and Audit/Certification. To date, the Development Stage has been completed and the implementation stage initiated, with the following successes: • Allocation of manpower resources in the formation of the Steering and Executive Management Committees, and an EMS Project Team; • Training of project personnel in ISO 14001/10/11/12 Standards; • Revision of the company Environmental Policy statement; • Conducting the Gap Analysis Study; • Development of the Implementation Plan based on the results of the Gap Analysis; • Initiated the development of the EMS manual and drafted initial procedures as outlined in the Implementation Plan; • Collection and analysis of data for identification of Significant Environmental Aspects and impacts for Exploration and Production activities. The Development and Implementation of an Environmental Management System in accordance with the world renowned ISO 14001 is expected to derive many benefits to Petrotrin such as reduced operating costs, improved financial performance, increased access to markets, improved environmental performance, improved community relations, improved customer relations and employee involvement and education.
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Xun, Qian, Peiliang Wang, Lidi Quan, and Yuxiang Xu. "Research on Control Strategy of Super Capacitor Energy Storage System in Traction Elevator." In 2016 International Conference on Engineering Science and Management. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/esm-16.2016.37.

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Wang, Fang, and Li Wang. "The Energy Conservation Design Strategy on Taiyuan South Railway Station Building." In 2017 7th International Conference on Education, Management, Computer and Society (EMCS 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/emcs-17.2017.309.

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Bader, B., O. Torres, J. A. Ortega, G. Lux, and J. L. Romeral. "Predictive real-time energy management strategy for PHEV using lookup-table-based Dynamic Programming." In 2013 World Electric Vehicle Symposium and Exhibition (EVS27). IEEE, 2013. http://dx.doi.org/10.1109/evs.2013.6914859.

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Solouk, Ali, Mahdi Shahbakhti, and Mohammad J. Mahjoob. "Energy Management and Control of a Hybrid Electric Vehicle With an Integrated Low Temperature Combustion (LTC) Engine." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6286.

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Low Temperature Combustion (LTC) provides a promising solution for clean energy-efficient engine technology which has not yet been utilized in Hybrid Electric Vehicle (HEV) engines. In this study, a variant of LTC engines, known as Homogeneous Charge Compression Ignition (HCCI), is utilized for operation in a series HEV configuration. An experimentally validated dynamic HCCI model is used to develop required engine torque-fuel consumption data. Given the importance of Energy Management Control (EMC) on HEV fuel economy, three different types of EMCs are designed and implemented. The EMC strategies incorporate three different control schemes including thermostatic Rule-Based Control (RBC), Dynamic Programming (DP), and Model Predictive Control (MPC). The simulation results are used to examine the fuel economy advantage of a series HEV with an integrated HCCI engine, compared to a conventional HEV with a modern Spark Ignition (SI) engine. The results show 12.6% improvement in fuel economy by using a HCCI engine in a HEV compared to a conventional HEV using a SI engine. In addition, the selection of EMC strategy is found to have a strong impact on vehicle fuel economy. EMC based on DP controller provides 15.3% fuel economy advantage over the RBC in a HEV with a HCCI engine.
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Reports on the topic "Energy Management Strategy (EMS)"

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EINHORN YAFFEE AND PRESCOTT WASHINGTON DC. Energy Management System (EMS) Study, Fort Belvoir, Virginia. Volume 1 of 2. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada330462.

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EINHORN YAFFEE AND PRESCOTT WASHINGTON DC. Energy Management System (EMS) Study, Fort Belvoir, Virginia. Volume 2 of 2. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada330587.

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Chambon, Paul H. PHEV Engine Control and Energy Management Strategy - FY12 Annual Report. Office of Scientific and Technical Information (OSTI), October 2012. http://dx.doi.org/10.2172/1081998.

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Chambon, Paul H. FY12 annual Report: PHEV Engine Control and Energy Management Strategy. Office of Scientific and Technical Information (OSTI), May 2012. http://dx.doi.org/10.2172/1042905.

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Chambon, Paul H. FY11 annual Report: PHEV Engine Control and Energy Management Strategy. Office of Scientific and Technical Information (OSTI), October 2011. http://dx.doi.org/10.2172/1035148.

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Makarov, Yuri V., Zhenyu Huang, Pavel V. Etingov, Jian Ma, Ross T. Guttromson, Krishnappa Subbarao, and Bhujanga B. Chakrabarti. Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations. Office of Scientific and Technical Information (OSTI), January 2010. http://dx.doi.org/10.2172/977321.

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Prescott, Einhorn Y. Energy Management System (EMS) Study, Fort Belvoir, Virginia, Department of the Army, Baltimore District, Corps of Engineers; Executive Summary. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada330358.

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Chiles, Thomas, Kenneth Shutika, and Philip Coleman. Riding the Electricity Market as an Energy Management Strategy: Savings from Real-Time Pricing. Office of Scientific and Technical Information (OSTI), June 2015. http://dx.doi.org/10.2172/1425679.

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Li, Yan, Yuhao Luo, and Xin Lu. PHEV Energy Management Optimization Based on Multi-Island Genetic Algorithm. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0739.

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The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor. Afterwards, based on the Isight and ADVISOR co-simulation platform, with the goal of ensuring certain dynamic performance and optimal fuel economy of the vehicle, the multi-island genetic algorithm is used to optimize the parameters of the membership function of the fuzzy control strategy to overcome it to a certain extent. The disadvantages of selecting parameters based on experience are compensated for, and the efficiency and feasibility of fuzzy control are improved. Finally, the PHEV vehicle model simulation comparison was carried out under the UDDS working condition through ADVISOR software. The optimization results show that while ensuring the required power performance, the vehicle fuzzy controller after parameter optimization using the multi-island genetic algorithm is more efficient, which can significantly reduce vehicle fuel consumption and improve exhaust emissions.
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Comparative Analysis on Fuel Consumption Between Two Online Strategies for P2 Hybrid Electric Vehicles: Adaptive-RuleBased (A-RB) vs Adaptive-Equivalent Consumption Minimization Strategy (A-ECMS). SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0740.

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Hybrid electric vehicles (HEVs) represent one of the main technological options for reducing vehicle CO2 emissions, helping car manufacturers (OEMs) to meet the stricter targets which are set by the European Green Deal for new passenger cars at 80 g CO2/km by 2025. The optimal power-split between the internal combustion engine (ICE) and the electric motor is a challenge since it depends on many unpredictable variables. In fact, HEV improvements in fuel economy and emissions strongly depend on the energy management strategy (EMS) on-board of the vehicle. Dynamic Programming approach (DP), direct methods and Pontryagin’s minimum principle (PMP) are some of the most used methodologies to optimize the HEV power-split. In this paper two online strategies are evaluated: an Adaptive-RuleBased (A-RB) and an Adaptive-Equivalent Consumption Minimization Strategy (A-ECMS). At first, a description of the P2 HEV model is made. Second, the two sub-optimal strategies are described in detail and then implemented on the HEV model to derive the fuel-optimal control strategy managing the power split between the thermal and electric engine to satisfy the driver's power request, including the engine on/off operating mode and the best gear selection. Finally, the two proposed strategies are tested on different driving cycles and then compared to other commercial strategies available in literature, such as the Equivalent Consumption Minimization Strategy (ECMS) and a RuleBased (RB) strategy. The results show that the A-ECMS is more conservative in terms of state of charge (SoC) compared to the A-RB. In fact, in the A-ECMS the SoC is always within the admissible range with considerable margin from the upper and lower limits for tested cycles, while in the A-RB a deep discharge of the battery is allowed. This behavior leads to a better fuel consumption of the A-RB compared to the A-ECMS, both in the WLTC and in the FTP-75 cycle.
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